ENIGMA- Addiction: Pooling of Existing Datasets to Identify Brain and Genetic Correlates of Addiction

ENIGMA-成瘾：汇集现有数据集以识别成瘾的大脑和遗传相关性

• 批准号: 10465044
• 负责人: Patricia Conrod
• 金额: $ 65.93万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10465044
• 关键词: Abstinence; Address; Adolescent; Adult; Age of Onset; Archives; Big Data; Biological Markers; Brain; Brain imaging; Candidate Disease Gene; Chronic; Collaborations; Communities; Country; Data; Data Analytics; Data Collection; Data Pooling; Data Set; Databases; Dependence; Development; Disease; Ensure; Failure; Funding; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genomics; Goals; Grant Review; Heritability; Institution; International; Investigation; Light; Link; Longevity; Magnetic Resonance Imaging; Measures; Medical Genetics; Mental Health; Meta-Analysis; Methods; Modality; Modeling; Nature; Neurobiology; Participant; Pattern; Pharmaceutical Preparations; Phenotype; Prefrontal Cortex; Privacy; Publishing; Quality Control; Reporting; Reproducibility; Research; Research Personnel; Rest; Risk; Sample Size; Sampling; Sampling Studies; Series; Sex Characteristics; Site; Smoking; Standardization; Structure; Substance abuse problem; Testing; Thick; United States National Institutes of Health; Validation; Weight; Work; addiction; analytical method; brain behavior; case control; cohort; comorbidity; data and analysis portal; data sharing; data standards; demographics; drug of abuse; genetic association; genome wide association study; imaging study; insight; interactive tool; multiple datasets; multiple drug use; neuroimaging; novel; opioid user; pleiotropism; predictive marker; psychiatric genomics; relating to nervous system; sociodemographics; substance use; success; tool; user-friendly; working group

PROJECT SUMMARY The ENIGMA consortium (http://enigma.ini.usc.edu/) was established to investigate brain structure, function, and disease by combining genomic and neuroimaging datasets from multiple sites. Its goal is to maximize statistical power and the yield from existing datasets through very large data pooling efforts. This goal has added importance today in light of concerns over the rigor and reproducibility of many neuroimaging and genomic findings. Since its initial successes (a genome wide association study on subcortical volumes with over 26,000 participants, Stein et al., 2012 Nature Genetics), a number of working groups have been established which use the standardized multi-site ENIGMA preprocessing pipelines and analytic methods to study the neurobiology of specific diseases. This application's PIs created the ENIGMA Addiction working group which now has access to datasets representing over 14000 participants. Genomic and neuromaging analyses on this unprecedented collection of data should produce important new insights into the neural and genetic basis of addiction. Building on our initial proof of concept funding (R21DA038381), we propose to further expand the Addiction working group which currently includes only a fraction of the world's potential relevant datasets, to identify robust brain markers of dependence for genetic association analyses, and to examine genetic and brain markers for the transition between stages of substance use across the lifespan. We will also increase the range of brain measures examined to include structural and functional connectivity (DTI and resting-state data) and will develop morphometric analyses of brain structures. We can use these biomarkers to assess if brain alterations preceded dependence or arose during early or chronic use and if these effects correct with abstinence by exploiting the familial, developmental, longitudinal and abstinence samples in our working group. We will create a data analysis portal that will provide both wide access to the pooled data and optimized analytic methods that maximize rigor and reproducibility (e.g., appropriate covariates, nested variance models, propensity weighting for sociodemographics, cross-validation) thereby guiding others to use these data appropriately and wisely. The analysis portal will archive analyses (e.g., exact subjects and analysis scripts) to ensure best practice and full transparency. We will actively work to expand the consortium to create a uniquely large neuroimaging-genetic addiction dataset and we will make results freely available to the research community through the online interactive tool ENIGMA-Vis (http://enigma.ini.usc.edu/enigma-vis/).

项目摘要 ENIGMA联盟（http：//enigma.ini.usc.edu/）的建立是为了研究大脑的结构、功能， 通过结合来自多个站点的基因组和神经成像数据集来研究疾病。它的目标是最大化 统计能力和通过非常大的数据池工作从现有数据集中获得的收益。这一目标 鉴于对许多神经成像的严格性和可重复性的担忧， 基因组发现自从其最初的成功（一项关于皮质下体积的全基因组关联研究， 超过26，000名参与者，Stein等人，2012 Nature Genetics），一些工作组已经 使用标准化的多站点ENIGMA预处理管道和分析方法， 研究特定疾病的神经生物学。这个应用程序的PI创建了ENIGMA成瘾工作 该小组现在可以访问代表14000多名参与者的数据集。基因组和神经成像 对这一前所未有的数据收集的分析应该产生重要的新见解， 以及成瘾的遗传基础。基于我们最初的概念验证资金（R21 DA 038381），我们建议 进一步扩大目前只包括世界上一小部分人的成瘾问题工作组， 潜在的相关数据集，以确定用于遗传关联分析的依赖性的强大大脑标记， 并检查遗传和大脑标记物，以了解药物使用阶段之间的过渡， 寿命我们还将增加大脑检查的范围，包括结构和功能 我们将继续研究脑组织的磁共振成像（DTI）和静息状态数据，并将开发大脑结构的形态测量分析。我们 可以使用这些生物标志物来评估大脑的改变是否先于依赖或在早期或 长期使用，如果这些影响通过利用家庭，发育， 纵向和禁欲样本在我们的工作组。我们将创建一个数据分析门户， 提供对汇集数据的广泛访问和优化的分析方法， 再现性（例如，适当的协变量，嵌套方差模型， 社会人口统计学、交叉验证），从而指导其他人适当和明智地使用这些数据。 分析门户将存档分析（例如，准确的主题和分析脚本），以确保最佳实践 完全透明。我们将积极努力扩大联合体，以创建一个独特的大型 神经成像-遗传成瘾数据集，我们将免费向研究社区提供结果 通过在线互动工具ENIGMA-Vis（http：//enigma.ini.usc.edu/enigma-vis/）。